face recognition based automated student attendance system chin howard universiti tunku abdul

83 Pages·2017·1.96 MB·English

by  Chin Eleen

FACE RECOGNITION BASED AUTOMATED
STUDENT ATTENDANCE SYSTEM

CHIN HOWARD

UNIVERSITI TUNKU ABDUL RAHMAN
FACE RECOGNITION BASED AUTOMATED STUDENT ATTENDANCE
SYSTEM

CHIN HOWARD

A project report submitted in partial fulfilment of the
requirements for the award of Bachelor of Engineering (Hons) Electronic
Engineering

Faculty of Engineering and Green Technology
Universiti Tunku Abdul Rahman

April 2018
ii

DECLARATION

I hereby declare that this project report is based on my original work except for
citations and quotations which have been duly acknowledged. I also declare that it has
not been previously and concurrently submitted for any other degree or award at
UTAR or other institutions.

Signature : _________________________

Name : CHIN HOWARD

ID No. : 13AGB03261

Date : _________________________
iii

APPROVAL FOR SUBMISSION

I certify that this project report entitled “FACE RECOGNITION BASED
AUTOMATED STUDENT ATTENDANCE SYSTEM” was prepared by CHIN
HOWARD has met the required standard for submission in partial fulfilment of the
requirements for the award of Bachelor of Engineering (Hons) Electronic Engineering
at Universiti Tunku Abdul Rahman.

Approved by,

Signature : _________________________

Supervisor : Dr. Humaira Nisar

Date : _________________________
iv

The copyright of this report belongs to the author under the terms of the
copyright Act 1987 as qualified by Intellectual Property Policy of Universiti Tunku
Abdul Rahman. Due acknowledgement shall always be made of the use of any
material contained in, or derived from, this report.

© 2018, CHIN HOWARD. All right reserved
v

ACKNOWLEDGEMENTS

I would like to thank everyone who had contributed to the successful completion of
this project. First, I would like to express my utmost gratitude to my research
supervisor, Dr. Humaira Nisar who in spite of being extraordinary busy with her duties,
took time to give invaluable advice and guidance throughout the development of the
research.

In addition, I would also like to express my deepest appreciation to my loving
parents and family members for their constant support and encouragement.

Last but not the least, I am grateful for the unselfish cooperation and assistance
that my friends had given me to complete this task.
vi

FACE RECOGNITION BASED AUTOMATED STUDENT ATTENDANCE
SYSTEM

ABSTRACT

Face is the representation of one’s identity. Hence, we have proposed an automated
student attendance system based on face recognition. Face recognition system is very
useful in life applications especially in security control systems. The airport protection
system uses face recognition to identify suspects and FBI (Federal Bureau of
Investigation) uses face recognition for criminal investigations. In our proposed
approach, firstly, video framing is performed by activating the camera through a user-
friendly interface. The face ROI is detected and segmented from the video frame by
using Viola-Jones algorithm. In the pre-processing stage, scaling of the size of images
is performed if necessary in order to prevent loss of information. The median filtering
is applied to remove noise followed by conversion of colour images to grayscale
images. After that, contrast-limited adaptive histogram equalization (CLAHE) is
implemented on images to enhance the contrast of images. In face recognition stage,
enhanced local binary pattern (LBP) and principal component analysis (PCA) is
applied correspondingly in order to extract the features from facial images. In our
proposed approach, the enhanced local binary pattern outperform the original LBP by
reducing the illumination effect and increasing the recognition rate. Next, the features
extracted from the test images are compared with the features extracted from the
training images. The facial images are then classified and recognized based on the best
result obtained from the combination of algorithm, enhanced LBP and PCA. Finally,
the attendance of the recognized student will be marked and saved in the excel file.
The student who is not registered will also be able to register on the spot and
notification will be given if students sign in more than once. The average accuracy of
recognition is 100 % for good quality images, 94.12 % of low-quality images and
95.76 % for Yale face database when two images per person are trained.
vii

TABLE OF CONTENTS

DECLARATION ii
APPROVAL FOR SUBMISSION iii
ACKNOWLEDGEMENTS v
ABSTRACT vi
TABLE OF CONTENTS vii
LIST OF TABLES x
LIST OF FIGURES xii
LIST OF SYMBOLS / ABBREVIATIONS xv
LIST OF APPENDICES xvii

CHAPTER
1 INTRODUCTION 1
1.1 Background 1
1.2 Problem Statement 3
1.3 Aims and Objectives 4
1.4 Thesis Organization 4
2 LITERATURE REVIEW 5
2.1 Student Attendance System 5
2.2 Face Detection 6
2.2.1 Viola-Jones Algorithm 10
2.3 Pre-Processing 12
2.4 Feature Extraction 16
2.4.1 Types of Feature Extraction 20
2.5 Feature Classification And Face Recognition 21
viii

2.6 Evaluation 22
3 METHODOLOGY 24
3.1 Methodology Flow 24
3.2 Input Images 27
3.2.1 Limitations of the Images 28
3.3 Face Detection 29
3.3.1 Pre-Processing 29
3.3.1.1 Scaling of Image 29
3.3.1.2 Median Filtering 30
3.3.1.3 Conversion to Grayscale Image 31
3.3.1.4 Contrast Limited Adaptive Histogram
Equalization 32
3.4 Feature Extraction 32
3.4.1 Working Principle of Original LBP 33
3.4.2 Working Principle of Proposed LBP 34
3.4.3 Working Principle of PCA 37
3.4.4 Feature Classification 40
3.4.5 Subjective Selection Algorithm and Face
Recognition 40
4 RESULT AND DISCUSSION 42
4.1 Result 42
4.2 Discussion 46
4.3 Comparison of LBP and PCA 50
4.4 Comparison with Previous Researches 51
4.5 Comparison with Luxand Face Recognition Application 54
4.6 Weakness of the Algorithm 55
4.7 Problems Faced and Solutions Taken 57
5 CONCLUSION AND RECOMMENDATION 58
5.1 Conclusion 58
5.2 Recommendation 59
ix

REFERENCES 60
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